6th
International Science, SocialSciences, Engineering and Energy Conference
17-19 December, 2014, Prajaktra Design Hotel, UdonThani, Thailand
I-SEEC 2014
http//iseec2014.udru.ac.th
Compression Depth Estimation for CPR manikin Based on
Accelerometers
Tasawan Puttasakul1,1, Supatra Wannasuk1,2, Manas Sangworasil1,3
1
Biomedical Engineering Program, Department of Physics, Faculty of Science
Rangsit University, Pathumtani 12000, Thailand
1
kang_2528@hotmail.com, 2supatha_bb@hotmail.com, 3ksamanas@gmail.com
Abstract
CPR (Cardio-Pulmonary Resuscitation) is the most common method for cardiac arrest patients. In order to evaluate
the effect of chest compressions accurately and increase survival rate, real time and accurate measurement of the
depth of compressions is necessary. The aim of this paper is to estimate the chest compression depth during CPR.
This method was based on two accelerometer, and built an experimental platform for monitoring compressions in
real time. Accuracy and processing of compression depth evaluation are derived from the difference in signal from
two acceleration using integral by trapezoidal techniques coding into a commercial embedded system called FiO
board. The result showed that the dual accelerometer was accurate and monitoring in real time able to improve
significantly the chest compression during CPR.
Keyword: CPR, chest compression depth, accelerometer
1. Introduction
Cardiopulmonary resuscitation, commonly known as CPR, is an emergency procedure performed in
an effort to manually preserve intact brain function until further measures are taken to restore spontaneous
blood circulation and breathing in a person who is in cardiac arrest. It is indicated in those who are
unresponsive with no breathing or abnormal breathing. According to the International Liaison Committee
on Resuscitation guidelines, CPR involves chest compressions at least 5 cm (2 inch) deep and at a rate of
at least 100 per minute in an effort to create artificial circulation by manually pumping blood through the
heart and thus the body. The rescuer may also provide breaths by either exhaling into the subject's mouth
or nose or using a device that pushes air into the subject's lungs. This process of externally providing
ventilation is termed artificial respiration. Current recommendations place emphasis on high-quality chest
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compressions over artificial respiration; a simplified CPR method involving chest compressions only is
recommended for untrained rescuers.
The aim of this study is to provide a feasible solution for determining the exact depth of chest
compression. In this study was based on two accelerometer, and built an experimental platform for
monitoring compressions in real time. Accuracy and processing of compression depth evaluation are
derived from the difference in signal from two acceleration using integral by trapezoidal techniques
coding into a commercial embedded system called FiO board.
2. Data Acquisition
In this paper, chest compression depth was measured from two acceleration sensors, at the top (acc1)
and bottom (acc2) of the chest of manikin as shown in Fig. 1.
Fig 1. Position acceleration sensors.
An acceleration sensors using ADXL335 which can convert voltage to acceleration as equation 1. The
signal from two accelerometers are sampled at 1000 Hz using commercial embedded system called FiO
board via to computer as shown in Fig. 2.
a = (V - Vzero)/330 mV/g
(1)
Where a is acceleration, V is voltage from acceleration sensors and Vzero is voltage at acceleration
equal 0 g
Fig 2. Chest compression depth monitoring system.
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3. Methodology
3.1 Algorithm of experiment
The estimation of chest compression depth get the real-time signal from two accelerometers which
one placed on the top of manikin, the other bottom the manikin. Acceleration offsets are removal by
subtracting between acc1 and acc2. Fig 3 shows the algorithm for the estimation of chest compression
depth. Integration is performed for the differential signal that is subtracted from the measured signal two
acceleration sensors. After first integration and second integration, the drift component is removed by detrend signal algorithm and then compression depth is detect from negative peaks.
acc1
+
integration
integration
De-trend
Compression
depth
Peak
detection
De-trend
acc2
Fig 3. Algorithm for the estimation of chest compression depth.
3.2 Discrete time integration
In order to find chest compression depth x(t), we must integrate the acceleration signal a(t) two times.
This operation takes place in discrete-time domain, referring to signals a(n) and x(n), where n is the
discrete index corresponding to time nT.
Starting with the continuous-time formulation the velocity signal v(t) can be written as
t
v(t)   a(  )d  v(t0 )
(2)
t0
The integral can be approximated by the trapezoidal formulation. Setting t = nT and t0 = (n-1)T,
Equation (2) can be converted to the discrete-time domain as
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v(n) 
T
(a(n)  a(n  1))  v(n  1)
2
(3)
Where T is the sample interval accounting for width of the trapez. So then calculate acceleration as
equation (4)
x(n) 
T
(v(n)  v(n  1))  x(n  1)
2
(4)
4. Result and Discussion
Real time estimate chest compression depth have been demonstrated using acceleration sensors and
algorithm based on commercial FiO embedded system by Simulink program. Fig 4 and 5 shows the
Simulink layout of the system and waveform from the accelerometer and its processed signal according
to the depth estimation algorithm.
Fig 4. the Simulink layout of the system.
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Fig 5. waveform from the accelerometer and its processed signal according to the depth estimation algorithm.
From Fig 5. shows the results of accelerometer signals, velocity and depth compression. The upper
waveform is the signal from difference two accelerometer sensor. After first integration is velocity shows
the middle waveform and the lower waveform is distant of chest compression.
Table 1. Comparing the depth when using one accelerometer and two accelerometer
Reference
(manikin)
Single Acc
Double
Acc
Floor
(x =1.75 inch)
1.73
1.69
1.71
Mattress
(x=2.00inch)
1.98
3.24
1.95
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Table 1. shows the summarized result of the experiment. Chest compressions on a manikin placed on
the floor about 60 seconds then break 5 minutes. After rest chest compressions on a manikin placed on the
mattress about 60 seconds and break again. From the result show that two accelerometer sensor better
than one accelerometer sensor while manikin placed on mattress because it reflects the depth of
penetration of the mattress. Fig 6. shows evaluation chest compression depth is designed by MATLAB
program.
Fig 6. evaluation chest compression depth windows.
5. Conclusion
In present study, we offered a method of estimation the chest compression depth during
CPR. This method was based on two accelerometer, and built an experimental platform for
monitoring compressions in real time. Accuracy and processing of compression depth
evaluation are derived from the difference in signal from two acceleration using integral by
trapezoidal techniques coding into a commercial embedded system called FiO board. The result
showed that the dual accelerometer was accurate and monitoring in real time able to improve
significantly the chest compression during CPR.
Acknowledgements
The authors thank head of biomedical engineering program, Rangsit university for their technical
support and comments help to improve this manuscript.
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References
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